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Besides the discussions on the theory, I think
we need a practical solution for Colin Badenhorst's initial problem (This is not
his problem only). He wants to compare two sets of spatial data with several
thousand samples.
Spatial autocorrelation (or lack of
independence) is a basic feature of spatial data, and thus we cannot do
anything to ask spatial data to behave well to satisfy the statistical
requirements. If your spatial data set is lack of spatial autocorrelation, you
may be asked to go back and take more samples. The ideal way is perhaps to develop a t-test (or whatever test) for
spatial data, something like "spatially weighted test". If such a test is not
available, we have no choice, but have to use existing methods. They
may not be exactly suitable to spatial data, but better than nothing.
For the time being, the best way to solve the
problem is still to use statistical methods, but try to explain the results
carefully and appropriately. We have to acknowledge the discrepancies between
the basic feature of spatial data and possible statistical requirements.
Meanwhile, when the sample size (well, going back to my initial concern) is
large, you will always get the result of rejecting the null hypothesis for REAL
data, no matter there is spatial dependence or not. In this case, what does such
a result mean? I would like to say this result is not very meaningful, as it
just proves the power of statistical tests. The simple ways of graphs (e.g.,
histogram, box-plot) and percentiles may become helpful for
comparison.
Therefore, for Colin's initial problem, the
solution is to explain the results properly, and maybe to try some other methods
if available.
Cheers,
Chaosheng
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Dr. Chaosheng Zhang Lecturer in GIS Department of Geography National University of Ireland, Galway IRELAND Tel: +353-91-524411 x 2375 Direct Tel: +353-91-49 2375 Fax: +353-91-525700 E-mail: [EMAIL PROTECTED] Web 1: www.nuigalway.ie/geography/zhang.html Web 2: www.nuigalway.ie/geography/gis/index.htm ---------------------------------------------------------------------------- |
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